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Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis

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  • Chen, Peipei
  • Wu, Yi
  • Zou, Lele

Abstract

China's photovoltaic power generation has experienced an ever-growing speed recently while fast expansion also exposed problems like insufficient power consumption and subsidy gaps in the finance of the government. Now the pilot project for promoting the distributed PV trading market was proposed to address the problem. Accordingly, this study simulates the trading market by analysing the economic behaviours of various agents in an expanded multi-agent-based model with extensions of local consumption principle and matchmaking bidding, which explores the interactions between agents, the trading market and the environment. This study documents that: (1) the trading market can be successfully implemented with the descending subsidy and the grid parity target; (2) the trading market can significantly facilitate local power consumption and release the burden of PV abandonment (e.g. the simulation implies that the abandonment rate of PV in the Gansu Province in 2018 could be reduced from 10.3% to 6%); (3) All firms gain considerable profits after the trading market introduced, and the government also achieves significant benefits from carbon emissions abatement; (4) the power grid suffers from negative margins while the downward trend would eventually end. This raises new perspectives on proposing proper incentive mechanisms like the system of permitted income.

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  • Chen, Peipei & Wu, Yi & Zou, Lele, 2019. "Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis," Energy, Elsevier, vol. 185(C), pages 423-436.
    • Handle: RePEc:eee:energy:v:185:y:2019:i:c:p:423-436
    DOI: 10.1016/j.energy.2019.07.070
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    Cited by:

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    2. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    3. Yu, Hui & Li, Ying & Wang, Wei, 2023. "Optimal innovation strategies of automakers with market competition under the dual-credit policy," Energy, Elsevier, vol. 283(C).
    4. Wu, Jiahui & Wang, Jidong & Kong, Xiangyu, 2022. "Strategic bidding in a competitive electricity market: An intelligent method using Multi-Agent Transfer Learning based on reinforcement learning," Energy, Elsevier, vol. 256(C).
    5. Song, Yazhi & Liu, Tiansen & Ye, Bin & Li, Yin, 2020. "Linking carbon market and electricity market for promoting the grid parity of photovoltaic electricity in China," Energy, Elsevier, vol. 211(C).
    6. Ying Wang & Lidan Tian & Junrong Xia & Weishi Zhang & Kaifeng Zhang, 2020. "Economic Assessment of the Peer-to-Peer Trading Policy of Distributed PV Electricity: A Case Study in China," Sustainability, MDPI, vol. 12(13), pages 1-22, June.

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